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May 15, 2014 - In addition, the YXXφ motif in CD151 was identified as a structural element that determines the trafficking of its associated integrins (Liu et al., ...

MBoC  |  ARTICLE

The membrane scaffold CD82 regulates cell adhesion by altering α4 integrin stability and molecular density Christina M. Terminia, Maura L. Cottera, Kristopher D. Marjona, Tione Burandaa, Keith A. Lidkeb, and Jennifer M. Gillettea a

Department of Pathology, University of New Mexico Health Sciences Center, and bDepartment of Physics and Astronomy, University of New Mexico, Albuquerque, NM 87131

ABSTRACT  Hematopoietic stem/progenitor cell (HSPC) interactions with the bone marrow microenvironment are important for maintaining HSPC self-renewal and differentiation. In recent work, we identified the tetraspanin protein, CD82, as a regulator of HPSC adhesion and homing to the bone marrow, although the mechanism by which CD82 mediated adhesion was unclear. In the present study, we determine that CD82 expression alters cell–matrix adhesion, as well as integrin surface expression. By combining the superresolution microscopy imaging technique, direct stochastic optical reconstruction microscopy, with protein clustering algorithms, we identify a critical role for CD82 in regulating the membrane organization of α4 integrin subunits. Our data demonstrate that CD82 overexpression increases the molecular density of α4 within membrane clusters, thereby increasing cellular adhesion. Furthermore, we find that the tight packing of α4 into membrane clusters depend on CD82 palmitoylation and the presence of α4 integrin ligands. In combination, these results provide unique quantifiable evidence of CD82’s contribution to the spatial arrangement of integrins within the plasma membrane and suggest that regulation of integrin density by tetraspanins is a critical component of cell adhesion.

Monitoring Editor Mark H. Ginsberg University of California, San Diego Received: Nov 13, 2013 Revised: Mar 3, 2014 Accepted: Mar 5, 2014

INTRODUCTION Cells receive signals or cues from their surrounding environment and respond so as to optimize survival, maintain quiescence, and promote proliferation, and differentiation. Stem cells, in particular, rely on physical interactions with their surrounding microenvironment or “niche” for the regulation and maintenance of their proper function. In the case of hematopoietic stem/progenitor cells (HSPCs),

This article was published online ahead of print in MBoC in Press (http://www .molbiolcell.org/cgi/doi/10.1091/mbc.E13-11-0660) on March 12, 2014. Address correspondence to: Jennifer M. Gillette ([email protected]). Abbreviations used: CD82KD, KG1a-knockdown cells created with CD82 short hairpin RNAs; CD82OE, KG1a cells that overexpress CD82 tagged with the mCherry fluorescent protein; dSTORM, direct stochastic optical reconstruction microscopy; ECM, extracellular matrix; HSPC, hematopoietic stem and progenitor cell; LDV, leucine–aspartic acid–valine sequence; Palm-CD82OE, KG1a cells that overexpress a palmitoylation-deficient form of CD82 tagged with the mCherry fluorescent protein; TEM, tetraspanin-enriched microdomain. © 2014 Termini et al. This article is distributed by The American Society for Cell Biology under license from the author(s). Two months after publication it is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0). “ASCB®,” “The American Society for Cell Biology®,” and “Molecular Biology of the Cell®” are registered trademarks of The American Society of Cell Biology.

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which reside primarily in the bone marrow, direct contact with the surrounding microenvironment is essential for regulating their proliferation, multipotentiation, and self-renewal (Zhang and Li, 2008; ter Huurne et  al., 2010). The bone marrow niche is a complex microenvironment consisting of a number of different cellular and extracellular matrix (ECM) components, including fibronectin and collagen I, III, and IV, as well as laminin (Klein, 1995). In addition to the bone marrow, HSPCs can traffic into and out of the peripheral blood, which is used clinically for stem cell isolation and transplantation. Furthermore, under stress conditions and/or injury, HSPCs can migrate to other tissues, such as spleen, liver, and even heart, to aid in tissue repair and remodeling (Taniguchi et al., 1996; Oostendorp et al., 2000; Losordo et al., 2011). However, molecular mechanisms orchestrating the interactions between HSPCs and various niche components are not well understood. The tetraspanins are a family of multispanning membrane scaffold proteins that regulate intercellular interactions. CD82 (also known as Kai1) is a member of the tetraspanin family of proteins, which are evolutionarily conserved proteins present in most eukaryotes and function in many aspects of cell physiology as mediators of cell adhesion, membrane trafficking, and cell signaling (Charrin Molecular Biology of the Cell

FIGURE 1:  CD82 expression mediates HSPC adhesion to fibronectin. Epifluorescence images depicting stable KG1a cell lines generated with (A) mCherry, (B) mCherry-CD82, and (C) mCherry-Palm-CD82 constructs. (D) The surface expression of CD82 was analyzed for each cell line using flow cytometry. (E) Cellular adhesion of each cell line was measured using a fluorescence-based adhesion assay. Cells were plated on FBS as a control or the indicated ECM proteins. To knock down CD82, KG1a cells were transfected with control siRNA, CD82 siRNA, and CD82 shRNA. The reduction of CD82 surface and total expression was measured by flow cytometry (F) and Western blot analysis (G). The adhesive abilities of these KD cells were then measured with the fluorescence adhesion assay (H). Error bars, SD; n ≥ 3 (*p < 0.05, **p < 0.01, ****p < 0.0001).

et al., 2009). One of the most distinct features of tetraspanins is their ability to associate in cis with other tetraspanins, integrins, members of the immunoglobulin superfamily of cell adhesion molecules, and signaling receptors, thereby forming tetraspanin-enriched microdomains (TEMs; Hemler, 2008; Charrin et al., 2009; Bassani and Cingolani, 2012). Formation of TEMs enables tetraspanins to serve as molecular facilitators or organizers for a number of transmembrane proteins. Tetraspanins also recruit and maintain intracellular signaling molecules in close proximity with membrane proteins, thus regulating downstream biochemical pathways (Hemler, 2005; Wang et al., 2007; Choi et al., 2009; Li et al., 2010). In its role as a protein scaffold, CD82 can form TEMs, hypothesized to be critical for the organization and function of several membrane proteins, including integrins (He et al., 2005; Han et al., 2012). Integrins are heterodimeric cell adhesion receptors consisting of one α- and one β-subunit and are expressed by all multicellular organisms. Components of the ECM, as well as specific cell surface receptors, serve as integrin ligands (Harburger and Calderwood, 2009; Barczyk et al., 2010). Integrins are capable of transmitting signals across the plasma membrane, which can promote cell migration, survival, differentiation and motility. Specifically, the α4 integrin, which is highly enriched in HSPCs, regulates HSPC migration, homing, proliferation, and differentiation (Coulombel et  al., 1997; Arroyo et al., 1999). Furthermore, previous studies in mice show that defects occur in HSPC homing and short-term engraftment upon conditional α4 knockout (Scott et al., 2003), and anti-α4β1antibodies mobilize HSPCs into the bloodstream (Papayannopoulou and Nakamoto, 1993). How CD82 can regulate integrin-mediated cellular and molecular functions including migration, adhesion, and signaling remains unclear. Furthermore, fundamental questions concerning the formation and regulation of TEMs and their potential modulation of integrin organization also still exist. Volume 25  May 15, 2014

Previous work from our lab identified CD82 as a regulator of HSPC homing and osteoblast adhesion (Larochelle et al., 2012). Using monoclonal antibodies specific to CD82, we demonstrated an inhibition of HSPC homing to the bone marrow and were able to reduce HSPC adhesion to osteoblasts. In the present study, we set out to identify the mechanism by which CD82 regulates HSPC niche interactions. We find that CD82 expression alters integrin expression by contributing to integrin stabilization on the plasma membrane via modulation of integrin internalization and recycling. Furthermore, we apply direct stochastic optical reconstruction microscopy (dSTORM) analysis to evaluate how CD82 and modifications in the palmitoylation sites of CD82 regulate the nanoscale clustering of integrins. Our data suggest that CD82 modulates the molecular packing of α4 molecules within clusters, thereby regulating the local molecular density of α4. As such, CD82 functionally regulates niche adhesion by modifying the organization of integrins into tightly packed clusters, which serves to strengthen cell adhesion to the ECM.

RESULTS CD82 expression regulates cell–matrix adhesion To begin analyzing the molecular mechanism by which CD82 regulates HSPC interactions with niche components, we generated a CD82-overexpressing cell line using human acute myelogenous leukemia progenitor-like cells, KG1a. We created a fusion protein in which CD82 was tagged with the mCherry fluorescent protein at the amino terminus (CD82OE). Stably transfected cells were selected and sorted. Figure 1B illustrates the plasma membrane and endosomal localization of mCherry-CD82, which is consistent with the localization of endogenously expressed CD82 (Larochelle et  al., 2012). A stably expressing mCherry control cell line (control) was also generated (Figure 1A). Flow cytometry analysis indicates a CD82 alters α4-mediated adhesion  |  1561 

twofold increase in CD82 surface expression between overexpressing and control cells (Figure 1D). Because our previous data suggested that CD82-specific antibodies alter in vitro adhesion and in vivo homing, we evaluated the CD82OE cells for changes in ECM adhesion. Using a fluorescence-based adhesion assay to quantify changes in cell–matrix adhesion to various substrates, we identified an increase in cell adhesion with CD82OE cells (Figure 1E). More specifically, we found that CD82OE cells display an increase in cell adhesion to laminin and an even greater increase in adhesion to fibronectin compared with control cells. Similarly, we found that reduction of CD82 expression could also affect cell–matrix adhesion. CD82-knockdown (CD82KD) cells were generated in the KG1a cell line using small interfering RNA (siRNA) and short hairpin RNA (shRNA). The CD82KD cells were found to express 0.005 μm2) is detected for Palm-CD82OE. In Figure 7E, we provide a visual reference for the length dimensions that would result in each of the square cluster areas. Taken together, these data indicate that there is a difference in the relative abundance of small and large α4 clusters between the overexpressing cell lines. Furthermore, these data suggest a functional difference between the ability of CD82OE and Palm-CD82OE cells to contribute to α4 cluster size. More specifically, the palmitoylation mutant form of CD82 is less effective at tightly packing the α4 molecules into a cluster, and so α4 clusters in Palm-CD82OE cells contain an increased proportion of clusters >0.005 μm2. In addition to identifying CD82-mediated changes in α4 cluster size, we also detected a difference in the spatial organization of α4 localizations within the clusters. To quantify these differences, we 1566  |  C. M. Termini et al.

calculated the number of α4 localizations per unit cluster area of the cell (0.01 μm2; Figure 7G). From these data, we found an increase in the average number of α4 localizations/0.01 μm2 in CD82OE cells, indicating an increase in the number of α4 molecules packed into a smaller area of the membrane. When we compare these results to the α4 packing in Palm-CD82OE cells, we find that Palm-CD82OE does not promote compact lateral packing of α4 molecules within clusters. Next we assessed whether the presence of α4 ligand has an effect on CD82-mediated changes in α4 density. To quantify this potential difference, we completed the dSTORM imaging and analysis on cells that were plated on VCAM-1 (α4 ligand) or N-cadherin (nonligand). Of interest, our data indicate that the increase in α4 density measured in CD82OE cells occurs only when the cells are plated on an α4 ligand (Figure 7G and Supplemental Figure S8, A–C and G). When the cells are plated on N-cadherin, we no longer detect a change in integrin density (Figure 7G and Supplemental Figure S8, D–F and H). In combination, these data suggest a critical role of tetraspanins in promoting the organization of integrins into adhesion complexes, which allows for proper cell–ECM interactions. More specifically, our data suggest that CD82 mediates the tight packing of α4 into clusters upon ligand engagement, which increases the molecular density of α4 and enhances cell–matrix adhesion. Furthermore, our data indicate that CD82 palmitoylation is required for the effective formation of tightly packed integrin clusters. Molecular Biology of the Cell

FIGURE 6:  CD82 expression regulates α4 molecular organization. Reconstructed dSTORM images of representative (A) control, (B) CD82OE, and (C) Palm-CD82OE cells plated on fibronectin and labeled fluorescently for α4. (D–F) The DBSCAN algorithm was used to examine cluster organization within a subregion of the cells. The DBSCAN parameters used were ε = 1 pixel and n = 30 localizations. Colored localizations denote localizations organized into a cluster, and gray localizations indicate molecules not organized, as they did not meet the DBSCAN parameters. (G) Quantification of the total clusters determined by DBSCAN. (H) Quantification of the percentage of α4 localizations determined to be organized into clusters. (I) Average number of α4 localizations per cluster as determined by DBSCAN. Error bars, SD; n = 4 cells (*p < 0.05, **p < 0.01).

DISCUSSION Tetraspanin–tetraspanin and tetraspanin–integrin interactions modify cell–cell and cell–matrix adhesion, although the molecular mechanisms that mediate these processes remain unclear. Our study provides strong evidence that the tetraspanin CD82 can regulate the membrane organization of integrins, resulting in the formation of tightly packed integrin “nanoclusters,” which increases matrix adhesion. Moreover, if we inhibit lateral CD82 clustering by overexpressing a mutant form of CD82, which cannot be palmitoylated, we diminish the organization and molecular packing of α4 integrins and ultimately block cell–matrix adhesion. These results led us to propose a model in which CD82 TEMs serve to regulate the molecular density of integrins by recruiting them into and/or stabilizing them within plasma membrane clusters in a ligand-dependent manner (Figure 8). Trafficking of HSPCs into and out of the bone marrow is essential throughout life to maintain homeostasis of the hematopoietic system and participate in innate immune responses. It is also critical in Volume 25  May 15, 2014

the clinical setting, where HSPCs can be isolated from normal donors and transplanted back into patients to replenish a compromised hematopoietic system. Previous work from our group identified the enrichment of CD82 at HSPC contact sites with osteoblasts, which led us to evaluate its potential role in HSPC/bone marrow interactions (Gillette et al., 2009). In a follow-up study, we found that treatment of human CD34+ cells with CD82 monoclonal antibodies inhibited CD34+ cell adhesion and homing to the bone marrow, although the mechanism for this CD82-mediated effect on adhesion and homing remained unknown (Larochelle et  al., 2012). In this study, we evaluate how CD82 expression regulates cell adhesion, with particular focus on modifications in integrin interactions. First, we demonstrate a role for CD82 in cell adhesion, finding that CD82OE increases cell adhesion to fibronectin, whereas CD82KD results in decreased adhesion. Of interest, it is important to note that this CD82-mediated increase in fibronectin adhesion requires palmitoylation of CD82, since an increase in cell adhesion was not observed with the Palm-CD82OE mutant. CD82 alters α4-mediated adhesion  |  1567 

FIGURE 7:  CD82 palmitoylation regulates the α4 density within clusters. Enlarged DBSCAN regions of (A) control, (B) CD82OE, and (C) Palm-CD82OE cells showing representative α4 clusters. (D) Cumulative distribution plot of the clusters compiled from n = 4 cells of each cell line plated on fibronectin; >250 clusters. Statistics determined using the Kolmogorov–Smirnov test. (E) Representative cluster geometry as depicted by black squares. Corresponding dimensions that give rise to cluster areas (0.0025, 0.005, and 0.01 μm2) drawn to scale of images in A–C. (F) Percentage of total clusters that fall within the cluster area bins determined for cells plated on fibronectin. (G) Average number of α4 molecular localizations/0.01 μm2 determined for cells plated on N-cadherin, fibronectin, and VCAM-1 using DBSCAN. Error bars, SD; n = 4 cells (*p < 0.05, **p < 0.01).

Previously the expression of CD82 was shown to modify adhesion through the αvβ3 (Ruseva et al., 2009) as well as the α6 (He et al., 2005) and β1 integrins (Jee et al., 2007). We went on to evaluate CD82-mediated differences in integrin surface expression and identified changes in α4 expression. Signaling through the α4β1 integrin is known to regulate HSPC adhesion and homing to the bone marrow (Hartz et al., 2011). For example, treatment of mice with α4-blocking antibodies results in HSPC mobilization into the blood (Papayannopoulou et al., 1995; Craddock et al., 1997). In addition, HSPC homing to the bone marrow is perturbed by α4blocking antibodies, indicating that α4 plays a role in regulating HSPC/bone marrow niche interactions (Papayannopoulou et  al., 1995). Our results demonstrate that the CD82-mediated increase in fibronectin adhesion occurs primarily through the α4β1 integrin. Furthermore, we identified an increase in surface expression of α4 with CD82OE or Palm-CD82OE and a decrease in surface expression with CD82KD. Studies show that tetraspanins can modulate integrin surface distribution and function through the regulation of integrin internalization (Winterwood et  al., 2006; Liu et  al., 2007) and trafficking through the endosomal pathway (Caswell et  al., 1568  |  C. M. Termini et al.

2009). Previous studies show that the rate of α3β1 internalization is significantly reduced in CD151-silenced cells (Winterwood et  al., 2006). In addition, the YXXφ motif in CD151 was identified as a structural element that determines the trafficking of its associated integrins (Liu et al., 2007). In both CD82OE and Palm-CD82OE cells, we detect a decreased rate of α4 internalization, as well as an increased rate of α4 recycling, compared with control cells. These data suggest that CD82OE can increase the surface expression of integrins, independent of palmitoylation status, by enhancing their plasma membrane recycling rate. In combination, these data implicate a mechanism for our measured expression increase of surface α4. However, it is clear that α4 expression alone cannot account for the observed change in cell adhesion, since CD82OE and PalmCD82OE cells express approximately the same amount of surface α4 yet have dramatically different adhesion abilities. TEMS have been proposed to enhance cell adhesion by clustering functionally related molecules or tightly packing specific receptors into the plasma membrane (Yanez-Mo et al., 2009). Palmitoylation can play a key role in the stable association of tetraspanins with each other (TEMs) and adhesion-related proteins. In fact, several Molecular Biology of the Cell

FIGURE 8:  Proposed model for CD82 regulation of molecular clustering and protein density. On the basis of superresolution microscopy data, we propose a model in which CD82 expression and clustering modulate α4 protein density. (A) CD82OE facilitates the membrane clustering of CD82, which leads to larger CD82 clusters and more tightly packed α4 clusters. The detected increase in α4 density upon CD82OE depends on α4 ligand engagement. In contrast, Palm-CD82OE is unable to promote effective TEMs, which results in smaller CD82 clusters and reduced molecular density of α4. (B) The spatial arrangement of molecules within TEMs is essential for organizing adequate adhesion and signaling platforms, which are weakened by palmitoylation site mutation. The association of CD82 with other tetraspanins, a process strengthened by tetraspanin palmitoylation, could indirectly affect the molecular density of α4 clusters by disrupting the organization within the TEM required to establish effective packing of α4 molecules. The increased α4 molecular density results in α4 molecules within close proximity of one another, which contributes to the overall strength and activity of the adhesion complex.

reports show that mutation of the intracellular membrane-proximal cysteines reduces interactions between tetraspanins (Berditchevski et al., 2002; Charrin et al., 2002; Yang et al., 2002, 2004; Zhou et al., 2004; Delandre et al., 2009; Stipp, 2010). In this study, we use the dSTORM superresolution imaging to visualize and quantitatively demonstrate palmitoylation-mediated alterations in tetraspanin organization. Our data indicate that mutation of the CD82 palmitoylation sites reduces the size of CD82 clusters within the plasma membrane and leads to changes in the membrane organization of the α4 integrin. Although previous work established that CD82 does not directly interact with α4β1 (Serru et al., 1999), it is clear that α4β1 and CD82 exist within the same membrane complex (Mannion et al., 1996). Therefore interaction of CD82 with other tetraspanins, which is stabilized by palmitoylation, likely contributes to the indirect linking of α4β1 into TEMs. Previous work showed that tetraspanin association facilitates the recruitment of cell adhesion molecules such as VCAM and intercellular adhesion molecule into adhesive “nanoclusters” (Barreiro et al., 2008). Similarly, CD81 was proposed to generate rapid adhesion strength to VCAM-1 through augmentation of α4β1 avidity (Feigelson et al., 2003). More recently, CD37 was also shown to regulate the mobility and clustering of α4β1 in B cells (van Spriel et al., 2012). Our clustering data indicate that CD82OE results in α4 clusters that are smaller in area than clusters found in control or Palm-CD82OE cells. However, despite the difference in average α4 cluster area, the number of α4 molecular localizations within each cluster remains the same. The molecular density of a protein cluster can be modified by altering the number of localizations found within a cluster area. Therefore fitting the same number of localizations into a smaller area results in the increased molecular density of α4 in CD82OE cells. The number and strength of bonds between integrins and ECM components can contribute to the overall strength of the adhesion complex (Maheshwari et al., 2000). Our data suggest that Volume 25  May 15, 2014

it is the tight packing of α4 molecules, as promoted by CD82 upon α4 ligand engagement, that enhances the overall adhesive contribution of α4 clusters. The increase in laminin adhesion by CD82OE cells may also suggest that CD82 alters the clustering and potentially the density of laminin-binding integrins α3 and α6. Previous studies assessing the importance of integrin spacing for adhesive contribution postulated that proper positioning is necessary to maintain integrin linkages with one another, as well as adequate integrin binding to ECM components (Arnold et al., 2004; SelhuberUnkel et al., 2008). Moreover, Arnold et al. (2004) showed that improper integrin-binding-site separation results in limited cell attachment due to restricted integrin clustering. In our study we find that CD82OE can facilitate the organization of α4 integrins into densely packed structures, implying the importance of α4 molecular density for cell adhesion. Furthermore, we speculate that the compromised α4 receptor clustering observed in Palm-CD82OE cells reduces adhesion by limiting the recruitment or stability of structural and/or signaling elements. Among the tetraspanins, CD82 is largely studied in cancer, where its expression is inversely correlated with metastasis formation (Miranti, 2009; Zoller, 2009; Tsai and Weissman, 2011). The ability of CD82 to regulate metastasis is likely related to its ability to modulate integrin function, which we demonstrate in this study involves molecular density regulation. Taken together, the results indicate that CD82 can modify not only the assembly of membrane protein structures, but also the molecular concentration of integrins within these structures. Thus we propose that the molecular crowding of α4, which is regulated by CD82 and its palmitoylation state, modulates the overall adhesive strength of cells to the ECM. Finally, our detailed insight into how CD82 contributes to the coordinated molecular regulation and organization of α4 implicates CD82 as an attractive potential therapeutic target to improve HSPC mobilization and engraftment capabilities. CD82 alters α4-mediated adhesion  |  1569 

MATERIALS AND METHODS Cell culture KG1a human hematopoietic myeloid progenitor cells (CCL-246.1; ATCC, Manassas, VA) were cultured in RPMI 1640 medium (Mediatech, Manassas, VA), supplemented with 10% fetal bovine serum (FBS; Invitrogen, Carlsbad, CA), 2 mM l-glutamine (Invitrogen), 100 U/ml penicillin, and 100 μg/ml streptomycin (PenStrep; Invitrogen). Human microvascular endothelial cells were purchased and cultured as indicated by Cell Applications (San Diego, CA). Cells were incubated at 37°C, 5% humidity, and 5% CO2.

Overexpression and knockdown vector constructs To generate the mCherry-CD82 plasmid, CD82 was subcloned from the YFP-CD82 construct (Addgene, Cambridge, MA) into the mCherry-C1 Vector (Invitrogen) using the XhoI and SacII restriction sites. The YFP-Palm-CD82 (CD82 palmitoylation mutant) construct was a generous gift from D. Derse (National Institutes of Health, Bethesda, MD; Mazurov et al., 2007). To increase the mCherry version of the construct, the PALM-CD82 insert was PCR amplified with the primers (forward) 5′-CTCGAGCGATGGGCTCAGCC-3′ and (reverse) 5′-CCGCGGAAGCTTTCAGTACTTGGG-3′ and inserted into the mCherry-C1 with the XhoI and SacII restriction enzymes. The CD82 shRNA plasmid (Santa Cruz Biotechnology, Santa Cruz, CA) consisted of a pool of three to five plasmids encoding 19–25 nucleotides (plus hairpin). CD82-targeted siRNAs consisting of pools of three 20- to 25-nucleotide siRNA sequences and the scrambled control siRNA were also purchased from Santa Cruz Biotechnology.

Nucleofection KG1a cells were transfected according to the manufacturer’s instructions using the Lonza Nucleofection Kit (Lonza, Walkersville, MD). Stable cell lines expressing mCherry, mCherry-CD82, and mCherryPalm–CD82 constructs were selected for with 500 μg/ml Geneticin (G418; Invitrogen). Stably expressing cells were isolated via fluorescence-activated cell sorting (University of New Mexico Facilities, Albuquerque, NM).

Flow cytometry Cells were labeled in PAB buffer (phosphate-buffered saline [PBS] + 1% BSA + 0.02% sodium azide) for 30 min on ice with either Alexa Fluor 647 CD82 (clone ASL-24; BioLegend, San Diego, CA), Alexa Flour 488 integrin α4 (clone 7.2R; R&D, Minneapolis, MN), FITC integrin α6 (clone GoH3; BioLegend), Alexa Flour 488 integrin α3 (clone ASC-1; BioLegend), APC integrin α5 (clone NKI-SAM-1; BioLegend), PE integrin α2 (clone HAS3; R&D), FITC integrin β7 (clone FIB27; BioLegend), or Alexa Flour 647 integrin β1 (clone TS2/16; BioLegend). Separate tubes of cells were labeled with Alexa Flour 488 mouse immunoglobulin G1 (IgG1), κ, isotype control (clone 11711; R&D), FITC rat IgG2a, κ, isotype control (clone RTK2758; BioLegend), Alexa Flour 647 mouse IgG1, κ, isotype control (clone MOPC-21; BioLegend), PE mouse IgG2a, κ, isotype control (clone MOPC-173; BioLegend), or APC mouse IgG2b, κ, isotype control (clone MPC-11; BioLegend). Cells were washed three times with PAB buffer and analyzed using Accuri C6 flow cytometer. Histograms were created using FlowJo software; fluorescence values were normalized to the mode.

Western blot and immunoprecipitation Cells were lysed in RIPA buffer. Protein concentration was determined using the bicinchoninic acid (BCA) assay (Pierce, Rockford, IL). We subjected 25 μg of protein to 8 or 10% SDS–PAGE. The proteins were then transferred to a nitrocellulose membrane. 1570  |  C. M. Termini et al.

Membranes were blocked with 5% dry milk in PBS with 0.22% Tween-20 for 1 h at room temperature. Membranes were then incubated with β-actin (clone AC-74; Sigma-Aldrich, St. Louis, MO), calnexin (clone C5C9, Cell Signaling, Beverly, MA), integrin α4 (clone EPR1355Y; Novus, Littleton, CO), integrin β1 (Cell Signaling), CD82 (clone ab66400; Abcam, Cambridge, MA), or integrin α6 (clone ab97760; Abcam) diluted in 5% milk/PBST overnight at 4°C. The membranes were washed three times for 10 min in PBS/0.22% Tween-20. Membranes were then incubated with peroxidase-conjugated AffiniPure goat anti-rabbit IgG or peroxidaseconjugated AffiniPure goat anti-mouse IgG secondary antibody diluted in 5% dry milk in PBS/0.22% Tween-20 for 1 h at room temperature. The membrane was washed three times for 10 min in PBS/0.22% Tween-20. Horseradish peroxidase conjugate enzymes were stimulated with SuperSignal West Pico Chemiluminescent Substrate (Pierce). Blots were imaged using the ChemiDoc XRS Imager (Bio-Rad, Hercules, CA) and analyzed using ImageJ (National Institutes of Health, Bethesda, MD) densitometry software. For immunoprecipitation experiments, BRIJ O10 cell lysates were incubated with CD82 antibody overnight at 4°C. Protein A/G Beads (Santa Cruz Biotechnology) were washed and added to the lysates for 30 min at room temperature. The supernatants were removed and the beads were washed three times before the beads and supernatants were analyzed for CD82 and α4 by Western blot as described.

Adhesion assay We coated 96-well microplates with fibronectin (10 μg/ml in PBS; Millipore, Billerica, MA), collagen I (10 μg/ml in PBS; Sigma-Aldrich), laminin (10 μg/ml in PBS; BD Biosciences, Franklin Lakes, NJ), or 10% FBS as control. For the VCAM-1 adhesion assay, 10 μg/ml recombinant VCAM-1 (R&D) was used to coat wells. Cells were labeled for 20 min with 2 μM calcein AM fluorescent dye (Invitrogen) in Hank’s buffered salt solution (HBSS). After two washes with HBSS, the cells were plated at 100,000 cells/well and incubated at 37°C for 2 h. The microplate was washed to remove nonadherent cells, and the remaining adherent cells were measured using a fluorescence plate reader with excitation wavelength of 488 nm and emission detected at 512 nm. Fluorescence data were then normalized to the mean fluorescence obtained for control cells. To measure α4β1specific adhesion, cells were either treated with dimethyl sulfoxide or blocked with the monovalent peptide LDV (1 μM), which was a generous gift from Larry Sklar and Tione Buranda (University of New Mexico).

Immunofluorescence Cells were fixed in 4% paraformaldehyde (PFA) and then blocked and permeabilized with PBS + 1.0% BSA + 0.1% Tween-20. Alexa Fluor 647-conjugated anti–human CD82 (clone ASL-24; BioLegend) and Alexa Fluor 488–conjugated anti–human integrin α4 (clone 7.2 R; R&D) were added to the sample. Immunofluorescence of VCAM-1 was completed with the mouse anti–human VCAM-1 primary antibody (AbD Serotec, Raleigh, NC) and the Alexa Fluor-488 goat anti-mouse secondary antibody (Life Technologies, Carlsbad, CA). Cells were labeled for 30 min. Cells were washed three times with PBS + 1% BSA and then imaged in an eight-well chamber slide. Cells were imaged by laser scanning confocal microscopy with a Zeiss Axiovert 100M inverted microscope (LSM 510) system (Carl Zeiss, Jena, Germany) using an excitation wavelength of 488 or 633 nm and a 63×/1.2 numerical aperture oil immersion objective. Image analysis was performed using the Zeiss LSM 510 software of ImageJ. Molecular Biology of the Cell

Internalization assay Cells were labeled for 1 h on ice using an Alexa Fluor 488 integrin α4 antibody (clone 7.2R; R&D). Cells were washed three times using cold medium and resuspended in RPMI medium. An aliquot of cells was used to determine median fluorescence using the Accuri C6 flow cytometer; this is considered 100% surface labeling. The remaining cells were put into the incubator (37°C, 5% CO2) for 10, 20, and 30 min. At the respective time point, 150,000 cells were moved to individual tubes. Cells were treated with 1 μg of anti– Alexa Fluor 488 antibody (clone A-11094; Invitrogen), which quenches surface fluorescence; cells were quenched on ice for 1 h, with >90% quenching efficiency. After quenching, cells were fixed for 20 min with 4% PFA. Median fluorescence in the FL-1 channel was read using Accuri C6 flow cytometer. Percentage internalized was calculated by dividing the median fluorescence intensity quenched value (normalized to background quench) by the median total α4 surface label intensity.

Recycling assay Cells were labeled for 30 min on ice using an Alexa Fluor 488 integrin α4 antibody (clone 7.2R; R&D). Cells were washed three times using cold medium and resuspended in RPMI medium. Before allowing internalization, two aliquots of cells were removed. The first was to determine 100% α4 surface labeling. The second aliquot was quenched and fixed; this aliquot represents the quenched background fluorescence. The remaining cells were put back into the incubator (37°C, 5% CO2) and allowed to internalize for 30 min. Cells were then treated with 1 μg of anti–Alexa Fluor 488 antibody (clone A-11094; Invitrogen) to quench surface fluorescence; cells were quenched on ice for 1 h. Cells were then moved back to the incubator (37°C, 5% CO2) and allowed to recycle for 30 min. After 30 min, the samples were moved back on ice and quenched again for 1 h. Cells were then fixed with 4% PFA, and median fluorescence was determined using Accuri C6 flow cytometer. The difference between the internalized value and the recycled value gives the amount of α4 recycled back to the membrane. To calculate recycled α4, the fluorescence intensity values were normalized. Because fluorescence recycling changes are relatively small, quenched background was subtracted from internalized and recycled median fluorescence values. Percentage of α4 recycled was calculated by taking the difference between the normalized internalized and normalized recycled fluorescence median values and dividing this number by the normalized internalized value. This gives the percentage of α4 that was labeled, allowed to internalize for 30 min, and quenched upon recycling back to the plasma membrane.

Superresolution imaging We used 25 μg/ml human plasma fibronectin (Millipore) diluted in PBS to coat the wells of an eight-well chamber slide for 20 min. We coated 15 μg/ml recombinant N-cadherin (R&D) diluted in PBS and wells for 30 min. We also used 10 μg/ml recombinant VCAM-1 (R&D) to coat wells for 1 h. Cells were then plated on the coated wells and incubated overnight at 37°C. The next day, cells were fixed with 4% PFA for 20 min, washed once with 1% BSA/PBS, and then blocked with 1% BSA/PBS for 1 h. For CD82 staining, cells were labeled with Alexa Fluor 647 anti–human CD82 antibody (1:125; clone ASL-24; BioLegend) diluted in 1% BSA/PBS. The wells were then washed three times with 1% BSA/PBS and fixed again with 4% PFA. For α4 staining, cells were first labeled with monoclonal α4 integrin primary antibody (1:200; clone Bu49; Thermo Scientific, Waltham, MA) diluted in 1% BSA/PBS for 1 h. The well was then washed three times Volume 25  May 15, 2014

with 1% BSA/PBS and subsequently labeled with goat anti-mouse Alexa Fluor 647 secondary antibody (1:200; Invitrogen) diluted in 1% BSA PBS for 1 h. The wells were then washed three times with 1% BSA/PBS and fixed again with 4% PFA. Labeled cells were imaged in a reducing buffer including 50 mM β-mercaptoethylamine as a reducing agent. Reference beads were used as a reference point to stabilize the sample during imaging; drift corrections were performed using MCL NanoDrive stage controller. The sample was imaged for 10,000 frames using the microscope setup previously described (Huang et al., 2011; van den Dries et al., 2013). After molecule localization estimates and uncertainties were obtained, superresolution images were reconstructed using MATLAB analyses (Huang et al., 2011). The pair autocorrelation function (Veatch et al., 2012) was used to analyze CD82 and α4 cluster size. Radially averaged autocorrelation functions were calculated from three 3 × 3 μm areas in each cell. Autocorrelation functions from the same cell were averaged and fitted to the functional form 2 2 ρ +g r >0 g g meas (r ) = B exp ( −r 2 4σ PSF ) (4πσ PSF ) ( ) PSF

To decouple cluster sizes from broadening due to finite localization precision, domains are evaluated as two-dimensional Gaussian shapes, giving 2 2 g(r > 0) = A exp ( −r 2 4σ Dom ) 4πσ Dom

and therefore the fitting function 2 2 ρ g meas (r ) = B exp ( −r 2 4σ PSF ) 4πσ PSF

{

2 2 + AB exp  −r 2 4 ( σ Dom + σ PSF )

}

2 2 4π ( σ Dom + σ PSF ) ρ + 1

Here A is the number of molecules per domain, B is the number of repeat observations per molecule, σPSF is the fluorophore localization precision, σDom is the cluster size, and ρ is the observed localization density. The value for ρ was calculated directly from the selected regions, and A, B, σPSF, and σDom were simultaneously estimated by performing a nonlinear least-squares fit of the average autocorrelation to gmeas(r). The magnitude of g(r) is a function of both the density and number of repeat observations of each molecule and can therefore differ with expression level, labeling efficiency, and imaging conditions, whereas the cluster size is extracted from the shape of the curve and is independent of these effects. The average cluster size for a population of cells was assessed statistically using Student’s unpaired t test. The DBSCAN cluster algorithm was used to assess larger-scale α4 clustering. A 56 × 56 pixel box (5.975 × 5.975 μm box) was examined for clustering. We used ε = 1 pixel (106.7 nm) and n = 30 localizations to examine α4 cluster area. To validate our parameters, we also tested the modified parameters ε = 0.5, n = 30 and ε = 0.5, n = 20 and saw the same trends of the cumulative distribution plots as assessed using the Kolmogorov–Smirnov test (Supplemental Figure S7, A and B).

Statistics All experiments were performed at least three times independently. Results are expressed as mean ± SD or SEM. Student’s t test was used for mean comparisons. The Kolmogorov–Smirnov test was used for comparison of cumulative distributions. Statistical analyses were performed using Prism 5 (GraphPad, La Jolla, CA). Significant differences are indicated using asterisks (*p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001). CD82 alters α4-mediated adhesion  |  1571 

ACKNOWLEDGMENTS We acknowledge all of the funding sources that made this work possible. This includes Grant 13SDG14630080 from the American Heart Association (to J.M.G.), an American Cancer Society Institutional Research Grant, a Research Allocations Committee grant from the University of New Mexico, pilot funding from the University of New Mexico Cancer Center and the University of New Mexico Signature Program in Cardiovascular Disease and Metabolism, and a graduate student fellowship from the New Mexico Spatiotemporal Modeling Center (to C.M.T.).

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CD82 alters α4-mediated adhesion  |  1573